Update evaluate.py

evaluate.py

@@ -5,13 +5,10 @@ import pygame
 import time
 
 def main():
-    # Create the environment
     env = TetrisEnv()
 
-    # Load the trained model
     model = DQN.load("models/dqn_tetris")
 
-    # Number of evaluation episodes
     episodes = 5
 
     for ep in range(1, episodes + 1):
@@ -19,19 +16,14 @@ def main():
         done = False
         total_reward = 0
         while not done:
-            # Render the game (optional)
             env.render(mode='human')
 
-            # Predict the action using the trained model
             action, _states = model.predict(obs, deterministic=True)
 
-            # Take the action in the environment
             obs, reward, done, info = env.step(action)
 
             total_reward += reward
-
-            # Control the rendering speed
-            pygame.time.wait(100)  # Wait 100 ms between steps
+            pygame.time.wait(100)  
 
         print(f"Episode {ep}: Total Reward = {total_reward}")